Tuesday, October 20, 2015

Machine learning algorithms grouped by similarity

Algorithms are often grouped by similarity in terms of their function (how they work). For example, tree-based methods, and neural network inspired methods.

I think this is the most useful way to group algorithms and it is the approach we will use here.

This is a useful grouping method, but it is not perfect. There are still algorithms that could just as easily fit into multiple categories like Learning Vector Quantization that is both a neural network inspired method and an instance-based method. There are also categories that have the same name that describes the problem and the class of algorithm such as Regression and Clustering.

We could handle these cases by listing algorithms twice or by selecting the group that subjectively is the “best” fit. I like this latter approach of not duplicating algorithms to keep things simple.

In this section I list many of the popular machine leaning algorithms grouped the way I think is the most intuitive. It is not exhaustive in either the groups or the algorithms, but I think it is representative and will be useful to you to get an idea of the lay of the land.

Please Note: There is a strong bias towards algorithms used for classification and regression, the two most prevalent supervised machine learning problems you will encounter.

Regression Algorithms

Regression is concerned with modelling the relationship between variables that is iteratively refined using a measure of error in the predictions made by the model.

Regression methods are a workhorse of statistics and have been cooped into statistical machine learning. This may be confusing because we can use regression to refer to the class of problem and the class of algorithm. Really, regression is a process.

The most popular regression algorithms are:

Ordinary Least Squares Regression (OLSR)

Linear Regression

Logistic Regression

Stepwise Regression

Multivariate Adaptive Regression Splines (MARS)

Locally Estimated Scatterplot Smoothing (LOESS)

Instance-based Algorithms

Instance based learning model a decision problem with instances or examples of training data that are deemed important or required to the model.

Such methods typically build up a database of example data and compare new data to the database using a similarity measure in order to find the best match and make a prediction. For this reason, instance-based methods are also called winner-take-all methods and memory-based learning. Focus is put on representation of the stored instances and similarity measures used between instances.

The most popular instance-based algorithms are:

k-Nearest Neighbour (kNN)

Learning Vector Quantization (LVQ)

Self-Organizing Map (SOM)

Locally Weighted Learning (LWL)

Regularization Algorithms

An extension made to another method (typically regression methods) that penalizes models based on their complexity, favoring simpler models that are also better at generalizing.

I have listed regularization algorithms separately here because they are popular, powerful and generally simple modifications made to other methods.

The most popular regularization algorithms are:

Ridge Regression

Least Absolute Shrinkage and Selection Operator (LASSO)

Elastic Net

Least-Angle Regression (LARS)

Decision Tree Algorithms

Decision tree methods construct a model of decisions made based on actual values of attributes in the data.

Decisions fork in tree structures until a prediction decision is made for a given record. Decision trees are trained on data for classification and regression problems. Decision trees are often fast and accurate and a big favorite in machine learning.

The most popular decision tree algorithms are:

Classification and Regression Tree (CART)

Iterative Dichotomiser 3 (ID3)

C4.5 and C5.0 (different versions of a powerful approach)

Chi-squared Automatic Interaction Detection (CHAID)

Decision Stump

M5

Conditional Decision Trees

Bayesian Algorithms

Bayesian methods are those that are explicitly apply Bayes’ Theorem for problems such as classification and regression.

The most popular Bayesian algorithms are:

Naive Bayes

Gaussian Naive Bayes

Multinomial Naive Bayes

Averaged One-Dependence Estimators (AODE)

Bayesian Belief Network (BBN)

Bayesian Network (BN)

Clustering Algorithms

Clustering, like regression describes the class of problem and the class of methods.

Clustering methods are typically organized by the modelling approaches such as centroid-based and hierarchal. All methods are concerned with using the inherent structures in the data to best organize the data into groups of maximum commonality.

The most popular clustering algorithms are:

k-Means

k-Medians

Expectation Maximisation (EM)

Hierarchical Clustering

Association Rule Learning Algorithms

Association rule learning are methods that extract rules that best explain observed relationships between variables in data.

These rules can discover important and commercially useful associations in large multidimensional datasets that can be exploited by an organisation.

The most popular association rule learning algorithms are:

Apriori algorithm

Eclat algorithm

Artificial Neural Network Algorithms

Artificial Neural Networks are models that are inspired by the structure and/or function of biological neural networks.

They are a class of pattern matching that are commonly used for regression and classification problems but are really an enormous subfield comprised of hundreds of algorithms and variations for all manner of problem types.

Note that I have separated out Deep Learning from neural networks because of the massive growth and popularity in the field. Here we are concerned with the more classical methods.

The most popular artificial neural network algorithms are:

Perceptron

Back-Propagation

Hopfield Network

Radial Basis Function Network (RBFN)

Deep Learning Algorithms

Deep Learning methods are a modern update to Artificial Neural Networks that exploit abundant cheap computation.

They are concerned with building much larger and more complex neural networks, and as commented above, many methods are concerned with semi-supervised learning problems where large datasets contain very little labelled data.

The most popular deep learning algorithms are:

Deep Boltzmann Machine (DBM)

Deep Belief Networks (DBN)

Convolutional Neural Network (CNN)

Stacked Auto-Encoders

Dimensionality Reduction Algorithms

Like clustering methods, dimensionality reduction seek and exploit the inherent structure in the data, but in this case in an unsupervised manner or order to summarise or describe data using less information.

This can be useful to visualize dimensional data or to simplify data which can then be used in a supervized learning method. Many of these methods can be adapted for use in classification and regression.

Principal Component Analysis (PCA)

Principal Component Regression (PCR)

Partial Least Squares Regression (PLSR)

Sammon Mapping

Multidimensional Scaling (MDS)

Projection Pursuit

Linear Discriminant Analysis (LDA)

Mixture Discriminant Analysis (MDA)

Quadratic Discriminant Analysis (QDA)

Flexible Discriminant Analysis (FDA)

Ensemble Algorithms

Ensemble methods are models composed of multiple weaker models that are independently trained and whose predictions are combined in some way to make the overall prediction.

Much effort is put into what types of weak learners to combine and the ways in which to combine them. This is a very powerful class of techniques and as such is very popular.